Identifying and utilizing the availability of enterprise resources

ABSTRACT

Various embodiments are directed to techniques for organizing fulfillment of enterprise products, such as by using a request manager informed by enterprise resources and resource utilization to recommend a facility to fulfill a product request. Some embodiments are directed to identifying a product request and determining the equipment and skills necessary to fulfill the product request. Based on this information and location data, embodiments may determine a set of facilities as candidates to fulfill the request. A machine learning model may be used to analyze current resource utilization of the facilities and predict facility availability and estimated completion times for the request fulfillment. A candidate facility may be recommended for fulfillment of a request based on facility availability and estimated completion times. In some embodiments, historical resource utilization may be used to inform further staffing and equipment service decisions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/517,270, entitled “IDENTIFYING AND UTILIZING THE AVAILABILITY OFENTERPRISE RESOURCES” filed on Jul. 19, 2019. The contents of theaforementioned application are incorporated herein by reference in theirentirety.

BACKGROUND

Enterprise resources may include or refer to devices, skills,techniques, employees, vendors, or the like used by an enterprise toprovide enterprise-offered products. An enterprise may refer to anentity or set of entities, such as businesses or companies, that engagesin economic activity to provide products such as goods or services. Forexample, a bank or banking company may be an enterprise, and productsoffered by a bank may include financial advising, wealth management,safe deposit boxes, savings accounts, money market accounts, currentaccounts, individual retirement accounts, fixed deposit accounts,recurring deposit accounts, certificates of deposit, time deposits,mutual funds, mortgages, personal loans, check books, credit cards,debit cards, service by automated teller machines (ATMs), and ATM cards.In order for an enterprise product to be provided, enterprise resourcesto provide the product must be available. For example, in order for abank to provide a safe deposit box, a safe deposit box resource must beavailable.

SUMMARY

Various embodiments described herein may include a device, a system, andan apparatus, and so forth including a memory to store instructions, andprocessing circuitry, coupled with the memory. The processing circuitryis operable to execute the instructions, that when executed, cause theprocessing circuitry to identify a request for a product from a user,determine two or more requisite resources to provide the productrequested by the user, the two or more requisite resources comprising atleast one device and at least one skill useful for fulfilling therequest for a product, and identify a location associated with the userbased on the request for the product from the user. Various embodimentsmay determine a set of candidate facilities to fulfill the request forthe product based on the two or more requisite resources to provide theproduct requested by the user and the location associated with the user,wherein each candidate facility in the set of candidate facilities tofulfill the request for the product comprises each of the two or morerequisite resources to provide the product requested by the user andsatisfy a proximity threshold to the location associated with the user,and compute a set of current resource utilizations for each candidatefacility in the set of candidate facilities, wherein the set of currentresource utilizations for a respective candidate facility includes anavailability of at least one of the two or more requisite resources toprovide the product requested by the user. In embodiments, theprocessing circuitry may evaluate the set of current resourceutilizations for each candidate facility in the set of candidatefacilities with a machine learning model to determine a set of availablefacilities and an estimated completion time to fulfill the request foreach available facility in the set of available facilities, determineone or more recommended facilities based on the set of availablefacilities and the estimated completion time to fulfill the request foreach available facility in the set of available facilities, and generateoutput comprising the one or more recommended facilities.

Various embodiments also include a computer-readable medium comprising aset of instructions that, in response to being executed by a processorcircuit, cause the processor circuit to determine two or more requisiteresources to provide a product based on a request for the product from auser, the two or more requisite resources comprising at least one deviceand at least one skill, and identify a location associated with the userbased on the request for the product from the user. In embodiments, theinstructions may determine a set of candidate facilities to fulfill therequest for the product based on the two or more requisite resources toprovide the product requested by the user and the location associatedwith the user, wherein each candidate facility in the set of candidatefacilities to fulfill the request for the product comprises each of thetwo or more requisite resources to provide the product requested by theuser and satisfy a proximity threshold to the location associated withthe user, and compute a set of current resource utilizations for eachcandidate facility in the set of candidate facilities, wherein the setof current resource utilizations for a respective candidate facilityincludes an availability of at least one of the two or more requisiteresources to provide the product requested by the user. In embodiments,the instructions may evaluate the set of current resource utilizationsfor each candidate facility in the set of candidate facilities with oneor more machine learning model to determine a set of availablefacilities and an estimated completion time to fulfill the request foreach available facility in the set of available facilities, determineone or more recommended facilities based on the set of availablefacilities and the estimated completion time to fulfill the request foreach available facility in the set of available facilities, and providethe one or more recommended facilities as output.

Embodiments, discussed herein, also include a computer-implementedmethod, including identifying a request for a product from a user,determining two or more requisite resources to provide the productrequested by the user, the two or more requisite resources comprising atleast one device and at least one skill, and identifying a locationassociated with the user based on the request for the product from theuser. In embodiments, the method includes determining a set of candidatefacilities to fulfill the request for the product based on the two ormore requisite resources to provide the product requested by the userand the location associated with the user, wherein each candidatefacility in the set of candidate facilities to fulfill the request forthe product comprises each of the two or more requisite resources toprovide the product requested by the user and satisfy a proximitythreshold to the location associated with the user, and computing a setof current resource utilizations for each candidate facility in the setof candidate facilities, wherein the set of current resourceutilizations for a respective candidate facility includes anavailability of at least one of the two or more requisite resources toprovide the product requested by the user. In embodiments, the methodincludes evaluating the set of current resource utilizations for eachcandidate facility in the set of candidate facilities with one or moremachine learning models to determine a set of available facilities andan estimated completion time to fulfill the request for each availablefacility in the set of available facilities and determining one or morerecommended facilities to provide as output based on the set ofavailable facilities and the estimated completion time to fulfill therequest for each available facility in the set of available facilities.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates exemplary aspects of a system flow according to oneor more embodiments described herein.

FIG. 2 illustrates exemplary aspects of processing a product requestaccording to one or more embodiments described herein.

FIG. 3A illustrates exemplary aspects of identifying candidatefacilities according to one or more embodiments described herein.

FIG. 3B illustrates exemplary aspects of processing resourceutilizations according to one or more embodiments described herein.

FIG. 4A illustrates exemplary aspects of using historical resourceutilizations according to one or more embodiments described herein.

FIG. 4B illustrates exemplary aspects of using historical resourceutilizations according to one or more embodiments described herein.

FIG. 5A illustrates an embodiment of a first logic flow according to oneor more embodiments described herein.

FIG. 5B illustrates an embodiment of a second logic flow according toone or more embodiments described herein.

FIG. 5C illustrates an embodiment of a third logic flow according to oneor more embodiments described herein.

FIG. 6 illustrates an embodiment of a computing architecture accordingto one or more embodiments described herein.

FIG. 7 illustrates an embodiment of a communications architectureaccording to one or more embodiments described herein.

DETAILED DESCRIPTION

Various embodiments are generally directed to techniques for fulfillinguser requests for enterprise products, for example, banking productsand/or services. Embodiments include using a request manager informed byenterprise facility resources and resource utilization to recommend afacility for the fulfillment of a product request. Some embodiments areparticularly directed to identifying a product request and determiningthe equipment and skill set necessary to fulfill the product request.Based on this information and location data, such embodiments arefurther directed to determine a set of facilities as candidates for therequest fulfillment. In some embodiments, a machine learning model isused to analyze current resource utilization of the facilities andpredict facility availability and estimated service times for thefulfillment of the request. In various embodiments, a candidate facilitymay be recommended for fulfillment of a request based on facilityavailability and estimated service times. In some embodiments,historical resource utilization may be used to inform staffing andequipment service decisions to maximize availability of facilities.These and other embodiments are described and claimed.

Various enterprises often provide customers with physical products aspart of their services. For example, a bank may provide a customer witha debit card, credit card, ATM card, or check book for access to fundsin the customer's account, a cashier's check for access to fundsguaranteed by the bank, or cash as part of a withdrawal. However,challenges exist in facilitating ready access to enterprise products.

For example, a customer may request a product in person from a bank,visiting a branch location, kiosk, bank café, or other physical facilityassociated with the bank. Such a process can be timely and intrusive tothe customer's schedule. For example, a bank branch may only be openduring specific business hours, during which the customer may beoccupied in another location. Further, even if able to make the visit, acustomer may be required to wait either in a line of customers waitingfor service from a bank employee or for the processing of the requestitself by a bank employee. Furthermore, not every bank facility maypossess the equipment or supplies necessary to service the request, forexample, a card printer or ink, or be always staffed by employeestrained to operate such equipment. In this example, a customer maytravel to a bank facility only to find the facility unable to fulfilltheir request. Such inconveniences, among others, may contribute to adifficulty for customers to effectively access products offered by anenterprise.

Some enterprises may allow customers to make product requests of banksfrom remote locations. For example, a customer may call a bank'scustomer service line from a phone and make the request of an operator.Similarly, a request may be made over an online system, for example, abank website or a bank API on a customer's mobile device. The productmay then be mailed to a customer, but mail is slow, and the processingof the request and/or the mailing of the product may take unacceptableamounts of time. Further risks are introduced such as mail delays andshipping errors. A customer may then wait for at least days after aproduct request for the product to be delivered. After the delivery ofsome products, such as a debit or credit card, a customer may then stillneed to coordinate with an enterprise to activate the product.

However, further factors may affect the enterprise facility's ability tofulfill the request. If a certain facility services a high number ofcustomers in a given day or is understaffed, the employees may not havetime to process the request by the time the customer or a courierarrives to pick up the product. Furthermore, even if the request wasprocessed in time for the pickup, other customers at a busy enterprisefacility may be frustrated to watch as an incoming customer or courierseemingly cuts to the front of the line of service or as enterpriseemployees prioritize a queue of requests for customers not present atthe enterprise.

These challenges, among others, may characterize a frustrating productpipeline from enterprise to customer, leading to poor quality ofservice. Enterprise employees and customers may be inconvenienced bysignificant stalls and interruptions to their schedules, productdelivery may be delayed, and the experience of further customers andthird parties such as couriers may be negatively affected.

Various embodiments described herein can remedy one or more of thesechallenges and weaknesses. Particularly, embodiments described hereininclude components that can improve management of customers' requestsfor enterprise products with respect to various facilities. Inembodiments, at least one facility may be recommended to fulfill arequest for an enterprise product based on particular requirements ofthe request, capabilities of the facility, and current and historicalusages of facility resources. In various embodiments, historical usagesof facility resources may be used to inform further staffing andinfrastructure decisions of the enterprise. Components described hereinwill therefore increase efficiency of providing service to enterprisecustomers, minimize undue strain on enterprise employees, and enableenterprises to maximize utilization of their resources. Accordingly, invarious embodiments described herein, dynamic assignment of productrequests to facilities based on historical, current, and/or estimatedavailability of resources may be implemented in a practical applicationto increase capabilities and improve adaptability of enterprise systemsas a whole. Additionally, or alternatively, in several embodimentsdescribed herein, recommendation of enterprise resource utilizationstrategies based on historical, current, and/or future resourceavailability and/or utilization may be implemented in a practicalapplication to increase capabilities and improve adaptability ofenterprise systems.

In various embodiments, one or more of the components, techniques, oraspects described herein may be implemented via one or more computingdevices, resulting in increased capability and improved functioning ofthe computer devices. Specific and particular manners of automaticallymonitoring facility resource utilization, analyzing trends in resourceutilization, and/or dynamically distributing product requests may beprovided by the components described in various embodiments herein. Inseveral embodiments, expected behaviors and behaviors involving theupdate and management of enterprise facility resources may be performedindependently of software utilizing the request management via familiar,user-friendly interface objects.

In various embodiments, components, techniques, or aspects describedherein may be implemented as a set of rules that improvecomputer-related technology by allowing a function not previouslyperformable by a computer that enables an improved technological resultto be achieved. For example, generating an output comprising a facilityrecommended to fulfill a user's product request based on historicalresource utilization at a plurality of facilities may be an improvedtechnological result.

With general reference to notations and nomenclature used herein, one ormore portions of the detailed description which follows may be presentedin terms of program procedures executed on a computer or network ofcomputers. These procedural descriptions and representations are used bythose skilled in the art to most effectively convey the substances oftheir work to others skilled in the art. A procedure is here, andgenerally, conceived to be a self-consistent sequence of operationsleading to a desired result. These operations are those requiringphysical manipulations of physical quantities. Usually, though notnecessarily, these quantities take the form of electrical, magnetic, oroptical signals capable of being stored, transferred, combined,compared, and otherwise manipulated. It proves convenient at times,principally for reasons of common usage, to refer to these signals asbits, values, elements, symbols, characters, terms, numbers, or thelike. It should be noted, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to those quantities.

Further, these manipulations are often referred to in terms, such asadding or comparing, which are commonly associated with mentaloperations performed by a human operator. However, no such capability ofa human operator is necessary, or desirable in most cases, in any of theoperations described herein that form part of one or more embodiments.Rather, these operations are machine operations. Useful machines forperforming operations of various embodiments include general purposedigital computers as selectively activated or configured by a computerprogram stored within that is written in accordance with the teachingsherein, and/or include apparatus specially constructed for the requiredpurpose. Various embodiments also relate to apparatus or systems forperforming these operations. These apparatuses may be speciallyconstructed for the required purpose or may include a general-purposecomputer. The required structure for a variety of these machines will beapparent from the description given.

Reference is now made to the drawings, wherein like reference numeralsare used to refer to like elements throughout. In the followingdescription, for purpose of explanation, numerous specific details areset forth in order to provide a thorough understanding thereof. It maybe evident, however, that the novel embodiments can be practiced withoutthese specific details. In other instances, well known structures anddevices are shown in block diagram form to facilitate a descriptionthereof. The intention is to cover all modification, equivalents, andalternatives within the scope of the claims.

FIG. 1 illustrates exemplary aspects of a system flow 100 according toone or more embodiments described herein. The system flow may include aproduct request 102, a request manager 104, and recommended facilities106. Embodiments are not limited in this context.

The product request 102 may be based on input received via a userinterface, such as a graphical user interface (GUI) displayed on amobile device. In some embodiments, a user may be a customer of anenterprise, such as a bank. For example, the customer may be a user ofan online platform that may be associated with the bank. In someembodiments, the product request 102 may comprise a request for at leastone physical item or service. For example, the product may comprise anitem such as a debit card, a credit card, a check book, a cashier'scheck, or cash. Furthermore, the product may comprise a service such asfinancial advising. A user may make a product request 102 by using awebsite or application associated with the enterprise on a mobile deviceor computer, by way of example. In some embodiments, the request may bemade while a user is logged into an online account associated with theirused services at the enterprise. Methods of making the product request102 are not intended to be limited at this point.

The product request 102 may include information received from the user.For example, the user may input data using a user interface, such as aGUI, including their name, phone number, email address, location,product desired, and/or method of delivery, including direct pickup froma branch in a geographic area or courier delivery to an address. If acourier is to be used, the request may include identifying informationof the courier in the request. If the user made the request while loggedinto an online account associated with their used services at theenterprise, information about their account may be included in therequest. The product request 102 may also include information from othersources, including the mobile device or computer used to make therequest and other APIs and applications used by the user. For example,location data could be provided by the client's device or by athird-party global positioning system application (GPS).

The product request 102 may be identified by the request manager 104.The request manager 104 may receive the product request 102 in a securemanner, for example, encrypted, secure tunnel, and so forth. The requestmanager 104 may extract information from the request 102. For example,the request manager 104 may extract the product desired, the useridentity, the user location, the user account information, and deliverypreferences.

The request manager 104 may also include or have access to datacomprising enterprise facilities. Each enterprise facility may beassociated with data currently and historically specific to thatfacility. For instance, such data may include the facility address,services provided at a facility, the presence of equipment and supplyquantities needed to provide products, employees present at thefacility, employees logged into a system at a facility, such as anelectronic time card system, employees currently not engaged in or soonto be engaged in another task, and skills in which present employeeshave been trained. Such skills may include technical skills, such asusing a card printer, or soft skills, such as customer service. Furtherexamples of data associated with an enterprise facility include thenumber of customers at a facility in a waiting queue, regular trafficpatterns, prescheduled appointments, events scheduled at the facility,including events scheduled for customers and for employees, andappointments requested by walk-in customers. Further examples of dataassociated with an enterprise facility include utility statuses of thefacility, such as operating electricity, internet, and watercapabilities and rates, and the number of vehicles parked in a parkinglot for the facility, as detected by a parking meter, a security cameraor sensor, or satellite feed. One of ordinary skill in the art willrecognize that further data useful for assessing the capability of anenterprise facility to handle requests at a particular time may beincluded.

In some embodiments, the request manager 104 may have access to dataassociated with enterprise facilities via at least one applicationprogramming interface (API). Partner applications may be managed by theenterprise or by a third party. In some embodiments, informationrequested from a third party via API may comprise information that doesnot identify specific users, thereby maintaining user privacy. Forexample, data may be received via an API from a third-party GPS system.Such data may identify that a number X of users of the GPS system are enroute to the facility and/or that each of the X customers are expectedto arrive at the facility at a certain time. It will be understood thatvarious other methods may be taken to integrate data from third-partiesinto the analysis of the request manager 104 as described herein.

The request manager 104 may consider the product request 102 andidentify recommended facilities 106 to fulfill the product request 102.Further data associated with enterprise facilities may be used inconjunction with the product request 102 by the request manager 104 inidentifying the recommended facilities 106. In some embodiments, therecommended facilities 106 is a single enterprise facility best suitedto fulfill the product request based on specific information of theproduct request 102 and other information considered by the requestmanager 104. In some embodiments, the recommended facilities 106 mayinclude several enterprise facilities suited to fulfill the productrequest. In these embodiments, the several enterprise facilities may beranked or ordered according to their suitability. Suitability may bedetermined by the request manager 104 according to proximity of theenterprise facility to the user, the presence of equipment and suppliesat the facility to enable the fulfillment of the product request 102,the availability of employees at the enterprise facility who have theskills to fulfill the product request, the busyness of the enterprisefacility, and other factors known to the request manager 104.

A facility from the recommended facilities 106 may be selected tofulfill the product request 102. In some embodiments, the facilityselected to fulfill the product request 102 may be the most suitable asdetermined by the request manager 104. In other embodiments, severalrecommended facilities 106 may be suggested to the user. In suchembodiments, the user may select one facility from the severalrecommended facilities 106 to fulfill the request. In other embodiments,a facility may be selected to fulfill the product request 102 withfeedback from facilities among the recommended facilities 106.

FIG. 2 illustrates exemplary aspects of processing a product requestaccording to one or more embodiments described herein. In architecture200, a product request 102 may be received by a request manager 104. Therequest manager may comprise a processing system that includes one ormore computing devices that are interconnected via one or more networklinks, e.g., wired, wireless, fiber, etc. In some embodiments, therequest manager may be a distributed computing system with each servercomprising one or more cores to process information and data.Embodiments are not limited in this context.

The request manager 104 may include a request analyzer 210 which mayprocess the product request 102. The request analyzer 210 may identifyfrom the product request 102 information specific to the productrequest. For example, the request analyzer 210 may identify a productdesired, information identifying a user, or any combination of dataincluded in the product request 102.

The request analyzer 210 may identify data pertaining to location 212.Location 212 may describe a user location at the time the productrequest 102 was generated or be a separate location specified in theproduct request 102. For instance, a product request 102 may begenerated while a user is at their workplace, but the user may wish theproduct request 102 to be fulfilled near their home. In this case, thelocation 212 in the product request 102 may comprise the user's homeaddress. In some embodiments, location 212 may be gathered from thedevice from which the product request 102 was received, for example, byan internet protocol (IP) address of the device. Location 212 may begathered from third-party applications and services. For example, alocation 212 may be identified from a GPS navigation service.

In various embodiments, location 212 may comprise multiple locationsidentified via one or a combination of the methods described above. Forexample, if a user is travelling between a first location and a secondlocation, both locations may be provided as part of the product request102, determined according to location updates from the user's device, oridentified by a third-party service such as a GPS navigation system. Ifthe user is travelling a route with intermediate stops, the locations ofsuch stops may also be included in location 212.

The request analyzer 210 may identify requisite resources 214 necessaryor helpful for generating the product identified in the request.Requisite resources 214 may include devices 216 such as an automatedteller machine (ATM), a money counter, a vault, safety deposit boxes, acoffee machine, a certified check printer, a bank card encoder,internet-connected computers, card printers, cardstock, bank cardblanks, ink supply, check supply, supply, and other devices or materialsnecessary to manufacture, activate, or otherwise fulfill a request forthe requested product. Requisite resources 214 may identify skills 218that may be required or useful for an enterprise employee to have or betrained in to use devices 216 or otherwise fulfill the product request102. Skills 218 may include technical skills and/or soft skills. Skills218 may include, by way of example, training on a particular software ornetwork system, training to operate card printing hardware, skills inmortgage sales, small business banking, teller, public notary customerservice, financial counseling, or other training certification. Skills218 may include other skills recognized by a person of ordinary skill inthe art as being of benefit in performing other tasks related to anenterprise or enterprise facility. Skills 218 may be associated withpermissions or security levels.

The request analyzer 210 may use location 212 and requisite resources214 to identify a candidate facility set 220. The candidate facility set220 may comprise enterprise facilities under a threshold distance fromthe location 212 that possess the requisite resources 214 to fulfill theproduct request 102.

In some embodiments, the request manager 104 may consider feedback fromemployees concerning the product request. The product request 102 or anindication of the product request 102 may be forwarded to facilities inthe candidate facility set 220 or to at least one employee at such afacility, for example, via email or user interface. A receiving employeemay be recognized to possess the skills 218 necessary to fulfill theproduct request 102. In some embodiments, the receiving employee mayindicate availability or unavailability to fulfill a request. Thisindication may be received by a resource utilization analyzer 222 as aninput. In some embodiments, this indication may be used to directlyfilter a candidate facility set 220.

The request manager 104 may analyze a candidate facility set 220 usingthe resource utilization analyzer 222. The resource utilization analyzer222 may determine current resource utilizations 224 of the facilities inthe candidate facility set 220. Current resource utilizations 224 maypertain to requisite resources 214 for fulfilling the product request102, to other facility resources, or to any combination thereof. Forexample, if a product request 102 identifies that a new debit card isneeded, requisite resources 214 might be identified as several devices216 and skills 218 as follows: devices comprising a card printer, a cardblank, a computer for associating a new card with a user account andactivating it; and skills comprising training to operate a card printerincluding loading the printer with a card blank, training to associatenew cards with particular accounts, and training to activate cards. Insuch an example, a facility identified in the candidate facility set 220may be recognized by the resource utilization analyzer 222 as havingcurrent resource utilizations 224 pertaining to a separate user'sappointment scheduled for loan advisement, to a scheduled training forbank employees on mortgage offerings, and to a lack of card blank supplyat the facility. Current resource utilizations 224 may include data suchas regular traffic patterns, existing product requests, a size of acheck in queue at the facility, prescheduled appointments, activitiesscheduled at the branch including events scheduled for users and foremployees, and/or appointments requested by walk-in users. Furtherexamples of current resource utilizations include utility statuses ofthe facility, such as operating electricity, internet, and watercapabilities and rates. Further examples include the number of customersen route to the facility, the number of navigation systems with thefacility set as a destination, and the number of vehicles parked in aparking lot for the facility, as detected by a parking meter, a securitycamera or sensor, or satellite feed. One of ordinary skill in the artwill recognize that further data useful for assessing the currentutilization of facility resources may be included.

The request manager 104 may include a machine learning model 226 used tofurther assess the availability of facilities within the candidatefacility set 220 to fulfill the product request 102. The machinelearning model 226 may be used to probabilistically determine theavailability of facilities in the candidate facility set to fulfill theproduct request 102 by estimated completion times.

A facility recommender 228 may use a machine learning model 226 todetermine an available facility set 230 from the candidate facility set220. Estimated completion times 232 for the available facility set 230to be able to fulfill the product request 102 may be determinedaccording to the machine learning model. The facility recommender 228may use the available facility set 230 and estimated completion times232 to generate a recommended facility set 106.

FIG. 3A illustrates exemplary aspects of identifying candidatefacilities according to one or more embodiments described herein. Inarchitecture 300A, a product request 102 is analyzed by a requestanalyzer 210. The request analyzer 210 may generate a candidate facilityset 220 based on the product request 102. Embodiments are not limited inthis context.

Specifically, the request analyzer 210 may identify requisite resources214 for fulfilling the product request 102. Such requisite resources 214may comprise devices 216 and skills 218 useful for fulfilling theproduct request 102. The request analyzer 210 may further identifylocation 212 useful for determining the facility set. In someembodiments, location 212 may be identified from the product request102. In some embodiments, location 212 may comprise a geographic regionto be used to identify a candidate facility set.

The request analyzer 210 may receive additional user data 334 to informthe determination of the candidate facility set 220. User data 334 maybe collected from a user's device, user input received via a userinterface, such as a GUI displayed on a mobile device, or from othersources such as third-party platforms. User data 334 may includeposition data 335, which may comprise the user's current position, aseparate position, or several positions. User data 334 may furthercomprise a proximity threshold 337. A proximity threshold 337 may relateto the position data 335 and either be received as user input or bepreset as a default value.

The request analyzer 210 may receive facility data 336 to inform thedetermination of the candidate facility set 220. In some embodiments,the request analyzer 210 may be coupled with a storage system which maycontain data structures, such as one or more databases, to storeinformation and data including the facility data 336. In embodiments,the request analyzer may be coupled to the storage system via one ormore wired and/or wireless networking links. In some embodiments, thestorage system may be local to the request analyzer or in a differentlocation. The facility data 336 may contain data on n facilities 338-n,where n is a positive integer.

Facility data 336 may comprise data on all facilities associated with abank or be prefiltered to include facilities within a geographic region.In some embodiments, facility data 336 may be limited to a geographicregion relating to the user data 334 or the location 212 recognized bythe request analyzer 210. In some embodiments, n may be predetermined.In these embodiments, n facilities may be included in the facility data336 according to their proximity to the location 212 or the user data334 recognized by the request analyzer 210. For example, if n=10,facility data 336 may contain data for facilities 338-1 through 338-10for the 10 facilities closest to position data 335.

Data associated with facility 338-n may comprise data relevant for thatfacility, including devices 342-n and skills 340-n. Devices 342-n maycomprise devices, supplies, and/or other tangible resources available ata particular facility 338-n. Skills 340-n may comprise technical and/orsoft skills in which employees at facility 338-n are trained or areotherwise competent.

In various embodiments, the request analyzer 210 may generate acandidate facility set according to the requisite resources 214identified as relating to the product request 102, location 212, userdata 334, facility data 336, or any combination thereof. In someembodiments, the request analyzer 210 may select a candidate facilityset 220 such that candidate facilities meet each of the criteriaprovided. In other words, criteria provided to the request analyzer maybe used to conduct indexed searches for facilities suitable to beincluded in a candidate facility set 220. For example, facilities fromthe facility data 336 may be filtered according to the devices 216 andskills 218 required to fulfill the product request 102 and matched to ageneral location 212, particularly within a proximity threshold 337 of aposition data 335. Each of these factors may be used as a strict filter.

In some embodiments, the request analyzer 210 may rank and/or orderpotential facilities to be included in the candidate facility set 220according to their satisfaction of the received criteria. In such cases,the request analyzer 210 may give different weights to differentcriteria. Some criteria may be used to strictly filter, for example, thematching of devices 216 required to fulfill a product request 102 todevices 342-n present at facility 338-n may be necessary for facility338-n to be included in the candidate facility set 220. Such a weightfor the presence of required devices may be, for example, 1, while theweight corresponding to the absence of such may be, for example, 0.Other weights may be between such and assigned to give preferences tocriteria over others, according to a facility's satisfaction of thecriteria, or a combination thereof. For instance, if a facility 338-nfalls a short distance beyond a proximity threshold 337 with respect toa preferred location 212, the facility 338-n may be further consideredfor the candidate facility set 220. However, such a facility 338-n maybe assigned an intermediate weight such that it is not ranked as highlyas it would have been had it been located nearer to the location 212.Such weighting may be decided by set scales or determined by a machinelearning component trained on historical selections of facilities fromcandidate sets. Embodiments are not intended to be limited at this time.

The candidate facility set 220 generated by the request analyzer 210comprises at least one facility 338-n capable of fulfilling the productrequest 102. Facilities in the candidate facility set are within anoverall distance threshold from preferred location 212 and have therequisite resources 214 to fulfill the request. A person of ordinaryskill in the art will recognize that additional data specific tofacilities may be included in said database and that additional criteriamay be used to determine the set of candidate facilities. In someembodiments, the candidate facility set may contain several facilitieswhich meet the required criteria. In these embodiments, facilities maybe in an unordered list in the candidate facility set and/or rankedaccording to their closeness of fit to the criteria considered by therequest analyzer 210.

FIG. 3B illustrates exemplary aspects of processing resourceutilizations according to one or more embodiments described herein. Inarchitecture 300B, a resource utilization analyzer 222 may receive acandidate facility set 220. The candidate facility set 220 may include nfacilities, where n is a positive integer. Embodiments are not limitedin this context.

In some embodiments, the resource utilization analyzer 222 may becoupled with a storage system which may contain data structures, such asone or more databases, to store information and data including thestatus data 348. In embodiments, the resource utilization analyzer maybe coupled to the storage system via one or more wired and/or wirelessnetworking links. In some embodiments, the storage system may be localto the resource utilization analyzer or in a different location.

In some embodiments, the status data 348 may be updated in real-time ornear real-time. Updates to status data may be made by employeesregarding a facility's use of resources or automatically in response toreceived data from sensors associated with the facility and knownscheduled events, meetings, or other resource utilizations. In someembodiments, aspects of status data 348 may be updated in coordinationwith data received via at least one API, as described herein.

In response to receiving the candidate facility set 220, the resourceutilization analyzer 222 may receive status data 348 for the facilitiesin the candidate facility set 220. For each facility 338-n in thecandidate facility set 220, status data 348 may include current devicestatuses 350-n, skill statuses 352-n, and facility statuses 354-n.Device statuses 350-n may relate to the current operating status oravailability of the devices 342-n at the facility 338-n. For example, aprinter may be shown as being used, offline, or out of ink. Skillstatuses 352-n may show the availability of employees with skills 340-nregularly associated with a facility 338-n. For example, if a facilityhas one employee with training to operate a particular software system,but that employee is not at work or is occupied in another activity, thestatus of that skill may be shown as unavailable. Facility statuses354-n may relate to other operational details more generally related toa facility. For instance, if a facility 338-n is closed, such a facilitystatus 354-n may be shown as unavailable. Additionally, a facility 338-nmay be shown to be unavailable if certain resources relating to thegeneral operation of the facility are currently unavailable. Forinstance, if a facility is experiencing an electric power outage, such afacility status 354-n may be shown as unavailable. Status data 348 foreach facility may include timestamps of the most recent status updatefor each type of status. Alternatively, status data 348 may include atimestamp associated with the status collection.

The resource utilization analyzer 222 may analyze the status data 348with respect to the candidate facility set 220 to record currentresource utilizations 224. Current resource utilizations 224 maycomprise utilization of resources of at least one facility among thefacilities in the candidate facility set 220 at a particular time. Theutilization of resources may be determined with status data 348 receivedregarding that facility 338-n.

The current resource utilizations 224 may be stored with timestamps in aresource utilization datastore 344. In some embodiments, the resourceutilization datastore 344 may be a storage system which may contain datastructures, such as one or more databases, to store information and dataincluding the historical resource utilizations 346. In embodiments, theresource utilization database may be coupled to the system via one ormore wired and/or wireless networking links. In some embodiments, theresource utilization database may be local to the system or in adifferent location. The resource utilization datastore 344 may storecurrent resource utilizations 224 across time as historical resourceutilizations 346. Historical resource utilizations 346 may includetimestamps associated with logged resource utilization data.

In various embodiments, current resource utilizations 224 may beanalyzed using a machine learning model 226. The machine learning model226 may be trained on or built with historical resource utilizations 346and associated completion times of product requests. Historical resourceutilization may provide the machine learning model 226 with context forthe continued availability or unavailability of resources, where theresources have been recognized as available or unavailable forutilization as recognized by the resource utilization analyzer 222 incurrent resource utilizations 224.

FIG. 4A illustrates exemplary aspects of using historical resourceutilizations according to one or more embodiments described herein.Embodiments are not limited in this context.

In architecture 400A, historical resource utilizations 346 in theresource utilization datastore 344 may be received by a model trainer456. The model trainer 456 may use a machine learning algorithm 458-A toanalyze the historical resource utilizations 346. The model trainer 456may also consider less persistent data, for example, satellite data orother sensor- or internet-collected data recognizing current orimpending resource utilizations. Using the results, the model trainer456 may generate a machine learning model 426-A. A machine learningmodel may include the predicted availability of a facility to fulfill aproduct request 102 by considering not only current resourceutilizations 224, but also historic trends in availability. For example,a machine learning model 426-A may be able to recognize current resourceavailability in conjunction with estimated unavailability due toperiodic traffic trends, scheduled activity at the facility or scheduledemployee time away from the facility, upcoming holidays, and/or incominginclement weather. A machine learning model may include the estimatedcontinuity of availability or unavailability of a resource at a facilityaccording to the current context and historical trends of availabilityat that facility. Estimated continuity may comprise a probability orlikelihood of resource availability or unavailability.

In some embodiments, a model trainer 456 may process historical resourceutilizations 346 associated with a particular type of product request102. A machine learning algorithm 458-A may be used to create a machinelearning model 426-A that includes an estimated time of completion of aparticular type of product request 102 based on historical resourceutilizations 346 of a facility when that facility or at least one otherfacility has received a request for a similar product.

A facility recommender 228 may utilize at least one machine learningmodel 426-A to generate a recommended facility set 106 by assessing thelikelihood of the predicted availability or unavailability of resourcesat a facility in conjunction with the product request 102. The facilityrecommender may, in some embodiments, consider estimated times ofcompletion of product request 102 fulfillment. Such estimated times maybe part of a machine learning model 426-A, or they may be statically setor predetermined according to at least the type of product requested inthe product request 102.

The recommended facility set 106 may include facilities estimated by thefacility recommender 228 to be actually available for fulfillment of aproduct request 102. The recommended facility set 106 may comprisefacilities estimated to remain available to process the product requestfor the duration of the processing time of the request.

FIG. 4B illustrates exemplary aspects of using historical resourceutilizations according to one or more embodiments described herein.Embodiments are not limited in this context.

In architecture 400B, historical resource utilizations 346 may be usedto inform current and/or future staffing decisions for a location.Historical resource utilizations 346 in a resource utilization datastore344 may be provided to a model trainer 456. The model trainer mayutilize a machine learning algorithm 458-B designed to consider chronicresource demand, particularly with respect to device statuses and 350-nand skill statuses 352-n for a facility 338-n.

The machine learning algorithm 458-B may be used to create a machinelearning model 426-B particularly tuned to predict overuse or underuseof staff or devices in a particular location. In addition to consideringchronic workload at a facility, the machine learning algorithm 458-B mayconsider past and current updates to resource availability at afacility. For example, a facility that has historically had resourceutilization indicating an appropriate workload may be recognized asbeing understaffed if a roster of facility employees is updated to showan employee no longer works at there. In some embodiments, the machinelearning algorithm 458-B may consider data reflecting the use of otherfacilities. For example, a facility may be predicted to receive moretraffic and therefore have higher resource utilization if a nearbyfacility is closed. Estimated overuse or underuse of resources may beprobabilistically scored in a resource utilization score.

Based on the specific results of analysis using the machine learningmodel 426-B, the staffing recommender 460 may identify future staffingrequirements 462 for at least one location. Future staffing requirements462 may indicate an estimation of future resource utilization trends andindication of device and employee resources needed to support thatutilization.

The machine learning model 426-B may be used by a staffing recommender460 to determine if at least one facility is a good candidate forstaffing changes. Such a decision may be made by comparing at least oneresource utilization score to a to a threshold or by monitoring afacility's score over a period of time. For example, if a resourceutilization score has historically indicated that a facility operates athigh use but not overuse, but recent scores indicate higher resourceutilization than the historically regular precedent, the staffingrecommender 460 may identify the facility as a candidate for a staffingaddition or for a future staffing change. The recommended staffingchange may be identified in the future staffing requirements 462.

A staffing change in this context is intended to mean a change in eitherdevice or employee availability at a location. A staffing changerelating to devices, for example, may include the installation of a newcard printer at a location. A staffing change relating to employees, forexample, may include hiring a new employee at a facility who possessesor can be trained in a skill set relating to an identified resourceoverutilization at the facility.

In some embodiments, the staffing recommender 460 may consider data frommultiple facilities. Future staffing requirements 462 may indicatestaffing changes for multiple facilities in relation to each other. Forexample, if a first facility is staffed with a high number of employeesskilled in working with a particular software such that there is anunderutilization of their skills, but a nearby second facility isstaffed with a low number of employees with that skill such that thereis an overutilization of those employees, the staffing recommender 460may recognize these facilities in conjunction with one another. In thiscase, future staffing requirements 462 may suggest that at least oneemployee from the first facility with this skillset be transferred towork at the second facility.

In some embodiments, future staffing requirements 462 may be outputtedto an analytical body. Such an analytical body may comprise a computerrecord or a human supervisor able to implement recommendations forstaffing changes as identified by the future staffing requirements 462.Embodiments are not intended to be limited in this context.

FIG. 5A illustrates an embodiment of a first logic flow according to oneor more embodiments described herein. In some examples, the logic flowmay be used, for example, to generate a recommended facility set such asrecommended facility set 106 using the product request 102. The logicflow 500A is described with respect to product request 102 andrecommended facility set 106 for the purposes of illustration.Embodiments are not limited in this context.

The logic flow may begin at block 502. The system may identify a requestfor a product. The request may be identified from, for example, aproduct request 102.

Continuing to block 504, the system may determine two or more requisiteresources to provide the product of the product request, the two or morerequisite resources comprising at least one device and at least oneskill. For example, requisite resources may be identified as at leastone device 216 to be used to prepare the product and at least one skill218 required of an employee to prepare the product. The skill may, insome embodiments, pertain to the operation of the device.

Continuing to block 506, the system may identify a location based on therequest for the product. For example, the location may be location 212.In various embodiments, the location may be collected from a user'saccount on a web system associated with the bank. In other embodiments,the location may be received from a user via a user interface, such as aGUI displayed on a mobile device, or gathered from user data, forexample, user data 334. Location may comprise a single location, aplurality of locations, or a region between and/or associated with aplurality of locations.

Continuing to block 508, the system may determine a set of candidatefacilities to fulfill the request for the product based on the two ormore requisite resources to provide the requested product and thelocation, wherein each candidate facility in the set of candidatefacilities to fulfill the request for the product comprises each of thetwo or more requisite resources to provide the requested product andsatisfy a proximity threshold to the location. For example, a set ofcandidate facilities may be a candidate facility set 220. In variousembodiments, the candidate facilities may be selected from a greaterdatabase of facilities, each of which would be associated with thedevices present at the facility, the skills of the employees working atthe facility, and the facility location. A proximity threshold may bepreset, received from a user, for example, as part of the productrequest 102, or otherwise included in user data, such as user data 334.Embodiments are not intended to be limited in this manner.

Continuing to block 510, the system may compute a set of currentresource utilizations for each candidate facility in the set ofcandidate facilities, wherein the set of current resource utilizationsfor a respective candidate facility includes an availability of at leastone of the two or more requisite resources to provide the requestedproduct. In some embodiments, availability may be determined byconsidering the presence of supplies for devices included in requisiteresources, the functionality of such devices, the engagement of specificemployees with the skills specified in the requisite resources, or anycombination thereof. Details relating to resource availability may beaccessed from at least one table or database which may be updatedautomatically by an electronic monitoring system or updated manually byan employee. Unavailability of any of the requisite resources at acandidate facility may be reflected in the set of current resourceutilizations.

Continuing to block 512, the system may evaluate the set of currentresource utilizations for each candidate facility in the set ofcandidate facilities using a machine learning model to determine a setof available facilities and an estimated completion time to fulfill therequest for each available facility in the set of available facilities.In some embodiments, the machine learning model may be trained onhistorical resource utilization, such as historical resourceutilizations 346. Using the machine learning model, the system may beable to understand current resource utilization in the context ofhistorical resource utilization trends. This context may be used topredict the availability of a facility to fulfill a request for aproduct.

The data used to train the machine learning model may have historicalresource utilization data associated with completion times of requestsfor products. In some examples, the data used to train the machinelearning model may specifically be historical resource utilization datasimilar to the current resource utilization, historical resourceutilization data associated with requests for a product similar to thecurrent request for a product, or both. In various embodiments, thesystem may calculate an estimated completion for each facility among thefacilities in the set of candidate facilities to fulfill the request fora product based on the machine learning model.

Continuing to block 514, the system may determine one or morerecommended facilities based on the set of available facilities and theestimated completion time to fulfill the request for each availablefacility in the set of available facilities. In some embodiments,facilities may be first filtered by availability and then ranked orordered by estimated completion time. In some embodiments, all availablecandidate facilities may be so ordered by recommendation. In otherembodiments, only a certain number of facilities may be recommended. Inthese cases, only the facilities with estimated completion times in acertain range may be recommended or the facilities with the soonestestimated completion times may be recommended. Embodiments are notintended to be limited in this manner.

Continuing to block 516, the system may generate output comprising theone or more recommended facilities. For example, the output may be arecommended facility set 106. Output may comprise a list or array of therecommended facilities. In various embodiments, the output may includeestimated completion times for the product request with the facilities.In some examples, the output may order the facilities according to theestimated completion times.

FIG. 5B illustrates an embodiment of a second logic flow according toone or more embodiments described herein. In some examples, the logicflow may be used, for example, to generate a recommended facility setsuch as recommended facility set 106 using identified requisiteresources 214. The logic flow 500B is described with respect torequisite resources 214 and recommended facility set 106 for thepurposes of illustration. Embodiments are not limited in this context.

The logic flow may begin at block 520. The system may determine two ormore requisite resources to provide a product based on a request for theproduct, the two or more requisite resources comprising at least onedevice and at least one skill.

Continuing to block 522, the system may identify a location based on therequest for the product. For example, the location associated with theuser may be location 212 or location collected from user data 334.Location may be preset, received from the user, collected fromthird-party systems, or any combination thereof. Location may comprise asingle location, a plurality of locations, or a region between and/orassociated with a plurality of locations.

Continuing to block 524, the system may determine a set of candidatefacilities to fulfill the request for the product based on the two ormore requisite resources to provide the product requested by the userand the location, wherein each candidate facility in the set ofcandidate facilities to fulfill the request for the product compriseseach of the two or more requisite resources to provide the productrequested and satisfy a proximity threshold to the location. Forexample, a set of candidate facilities may be a candidate facility set220. In various embodiments, the candidate facilities may be selectedfrom a greater database of facilities, each of which would be associatedwith the devices present at the facility, the skills of the employeesworking at the facility, and the facility location. A proximitythreshold may be preset, received from the user, for example, as part ofthe product request 102, or otherwise included in user data, such asuser data 334. Embodiments are not intended to be limited in thismanner.

Continuing to block 526, the system may compute a set of currentresource utilizations for each candidate facility in the set ofcandidate facilities, wherein the set of current resource utilizationsfor a respective candidate facility includes an availability of at leastone of the two or more requisite resources to provide the requestedproduct. In some embodiments, resource availability may be determinedaccording to the presence of supplies for devices included in requisiteresources, the functionality of such devices, the engagement of specificemployees with the skills specified in the requisite resources, or anycombination thereof. Details relating to resource availability may beaccessed from at least one table or database which may be updatedautomatically by an electronic monitoring system or updated manually byan employee. Unavailability of any of the requisite resources at acandidate facility may be reflected in the set of current resourceutilizations.

Continuing to block 528, the system may evaluate the set of currentresource utilizations for each candidate facility in the set ofcandidate facilities using one or more machine learning techniques todetermine a set of available facilities and an estimated completion timeto fulfill the request for each available facility in the set ofavailable facilities. In some embodiments, machine learning techniquesmay utilize training on historical resource utilization.

Continuing to block 530, the system may determine one or morerecommended facilities based on the set of available facilities and theestimated completion time to fulfill the request for each availablefacility in the set of available facilities. Recommended facilities maybe associated with the respective completion times estimated for them tofulfill the request.

Continuing to block 532, the system may provide the one or morerecommended facilities as output. For example, the output may comprisethe recommended facility set 106.

In some embodiments, the output may be provided to the user via a userinterface, such as a GUI. In some embodiments, the user choice orverification of a recommended facility from the output may be receivedvia a user interface, such as a GUI. In response to the reception of auser selection or verification of a facility, the product request may besent to that facility. In some embodiments, at least one employee may benotified of the request, for example, via email. In some embodiments,the employee notified may be an employee with required skills tocomplete the request. In certain embodiments, the employee notified mayaccept the request for a product, in which case the user may benotified. In some embodiments, the employee notified may deny therequest, in which case the user may be prompted to select anotherfacility, in which case the request would be forwarded to an employee ofthat facility. In embodiments, the product request may be added to aqueue of tasks at the facility once accepted. In some embodiments, adevice at the facility may be set to automatically fulfill the productrequest.

FIG. 5C illustrates an embodiment of a third logic flow according to oneor more embodiments described herein. In some examples, the logic flowmay be used, for example, to generate a recommended facility set such asrecommended facility set 106 using the product request 102. The logicflow 500C is described with respect to product request 102 andrecommended facility set 106 for the purposes of illustration.Embodiments are not limited in this context.

The logic flow may begin at block 502. The system may begin byidentifying a request for a product. The request may be identified from,for example, a product request 102.

The system may continue to block 532, determining two or more requisiteresources to provide the requested product, the two or more requisiteresources comprising at least one device and at least one skill.

The system may continue to block 534, identifying a location based onthe product request. The system may continue to block 536, determining aset of candidate facilities to fulfill the request for the product basedon the two or more requisite resources to provide the requested productand the location, wherein each candidate facility in the set ofcandidate facilities to fulfill the request for the product compriseseach of the two or more requisite resources to provide the requestedproduct and satisfy a proximity threshold to the location.

The system may continue to block 538, computing a set of currentresource utilizations for each candidate facility in the set ofcandidate facilities, wherein the set of current resource utilizationsfor a respective candidate facility includes an availability of at leastone of the two or more requisite resources to provide the requestedproduct.

The system may continue to block 540, evaluating the set of currentresource utilizations for each candidate facility in the set ofcandidate facilities using a machine learning model to determine a setof available facilities and an estimated completion time to fulfill therequest for each available facility in the set of available facilities.

The system may continue to block 542, determining one or morerecommended facilities to provide as output based on the set ofavailable facilities and the estimated completion time to fulfill therequest for each available facility in the set of available facilities.

FIG. 6 illustrates an embodiment of an exemplary computing architecture600 that may be suitable for implementing various embodiments aspreviously described. In various embodiments, the computing architecture600 may comprise or be implemented as part of an electronic device. Insome embodiments, the computing architecture 600 may be representative,for example, of one or more component described herein. In someembodiments, computing architecture 600 may be representative, forexample, of a computing device that implements or utilizes one or moreof request manager 104, request analyzer 210, resource utilizationanalyzer 222, facility recommender 228, and/or one or more techniquesdescribed herein. Embodiments are not limited in this context.

As used in this application, the terms “system” and “component” and“module” are intended to refer to a computer-related entity, eitherhardware, a combination of hardware and software, software, or softwarein execution, examples of which are provided by the exemplary computingarchitecture 600. For example, a component can be, but is not limited tobeing, a process running on a processor, a processor, a hard disk drive,multiple storage drives (of optical and/or magnetic storage medium), anobject, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution, and a component canbe localized on one computer and/or distributed between two or morecomputers. Further, components may be communicatively coupled to eachother by various types of communications media to coordinate operations.The coordination may involve the uni-directional or bi-directionalexchange of information. For instance, the components may communicateinformation in the form of signals communicated over the communicationsmedia. The information can be implemented as signals allocated tovarious signal lines. In such allocations, each message is a signal.Further embodiments, however, may alternatively employ data messages.Such data messages may be sent across various connections. Exemplaryconnections include parallel interfaces, serial interfaces, and businterfaces.

The computing architecture 600 includes various common computingelements, such as one or more processors, multi-core processors,co-processors, memory units, chipsets, controllers, peripherals,interfaces, oscillators, timing devices, video cards, audio cards,multimedia input/output (I/O) components, power supplies, and so forth.The embodiments, however, are not limited to implementation by thecomputing architecture 600.

As shown in FIG. 6, the computing architecture 600 comprises aprocessing unit 604, a system memory 606 and a system bus 608. Theprocessing unit 604 can be any of various commercially availableprocessors, including without limitation an AMD® Athlon®, Duron® andOpteron® processors; ARM® application, embedded and secure processors;IBM® and Motorola® DragonBall® and PowerPC® processors; IBM and Sony®Cell processors; Intel® Celeron®, Core (2) Duo®, Itanium®, Pentium®,Xeon®, and XScale® processors; and similar processors. Dualmicroprocessors, multi-core processors, and other multi-processorarchitectures may also be employed as the processing unit 604.

The system bus 608 provides an interface for system componentsincluding, but not limited to, the system memory 606 to the processingunit 604. The system bus 608 can be any of several types of busstructure that may further interconnect to a memory bus (with or withouta memory controller), a peripheral bus, and a local bus using any of avariety of commercially available bus architectures. Interface adaptersmay connect to the system bus 608 via a slot architecture. Example slotarchitectures may include without limitation Accelerated Graphics Port(AGP), Card Bus, (Extended) Industry Standard Architecture ((E)ISA),Micro Channel Architecture (MCA), NuBus, Peripheral ComponentInterconnect (Extended) (PCI(X)), PCI Express, Personal Computer MemoryCard International Association (PCMCIA), and the like.

The system memory 606 may include various types of computer-readablestorage media in the form of one or more higher speed memory units, suchas read-only memory (ROM), random-access memory (RAM), dynamic RAM(DRAM), Double-Data-Rate DRAM (DDRAM), synchronous DRAM (SDRAM), staticRAM (SRAM), programmable ROM (PROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory (e.g., oneor more flash arrays), polymer memory such as ferroelectric polymermemory, ovonic memory, phase change or ferroelectric memory,silicon-oxide-nitride-oxide-silicon (SONOS) memory, magnetic or opticalcards, an array of devices such as Redundant Array of Independent Disks(RAID) drives, solid state memory devices (e.g., USB memory, solid statedrives (SSD) and any other type of storage media suitable for storinginformation. In the illustrated embodiment shown in FIG. 6, the systemmemory 606 can include non-volatile memory 610 and/or volatile memory612. In some embodiments, system memory 606 may include main memory. Abasic input/output system (BIOS) can be stored in the non-volatilememory 610.

The computer 602 may include various types of computer-readable storagemedia in the form of one or more lower speed memory units, including aninternal (or external) hard disk drive (HDD) 614, a magnetic floppy diskdrive (FDD) 616 to read from or write to a removable magnetic disk 618,and an optical disk drive 620 to read from or write to a removableoptical disk 622 (e.g., a CD-ROM or DVD). The HDD 614, FDD 616 andoptical disk drive 620 can be connected to the system bus 608 by a HDDinterface 624, an FDD interface 626 and an optical drive interface 628,respectively. The HDD interface 624 for external drive implementationscan include at least one or both of Universal Serial Bus (USB) andInstitute of Electrical and Electronics Engineers (IEEE) 694 interfacetechnologies. In various embodiments, these types of memory may not beincluded in main memory or system memory.

The drives and associated computer-readable media provide volatileand/or nonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For example, a number of program modules canbe stored in the drives and memory units 610, 612, including anoperating system 630, one or more application programs 632, otherprogram modules 634, and program data 636. In one embodiment, the one ormore application programs 632, other program modules 634, and programdata 636 can include or implement, for example, the various techniques,applications, and/or components described herein.

A user can enter commands and information into the computer 602 throughone or more wire/wireless input devices, for example, a keyboard 638 anda pointing device, such as a mouse 640. Other input devices may includemicrophones, infra-red (IR) remote controls, radio-frequency (RF) remotecontrols, game pads, stylus pens, card readers, dongles, finger printreaders, gloves, graphics tablets, joysticks, keyboards, retina readers,touch screens (e.g., capacitive, resistive, etc.), trackballs,trackpads, sensors, styluses, and the like. These and other inputdevices are often connected to the processing unit 604 through an inputdevice interface 642 that is coupled to the system bus 608, but can beconnected by other interfaces such as a parallel port, IEEE 994 serialport, a game port, a USB port, an IR interface, and so forth.

A monitor 644 or other type of display device is also connected to thesystem bus 608 via an interface, such as a video adaptor 646. Themonitor 644 may be internal or external to the computer 602. In additionto the monitor 644, a computer typically includes other peripheraloutput devices, such as speakers, printers, and so forth.

The computer 602 may operate in a networked environment using logicalconnections via wire and/or wireless communications to one or moreremote computers, such as a remote computer 648. In various embodiments,one or more migrations may occur via the networked environment. Theremote computer 648 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer602, although, for purposes of brevity, only a memory/storage device 650is illustrated. The logical connections depicted include wire/wirelessconnectivity to a local area network (LAN) 652 and/or larger networks,for example, a wide area network (WAN) 654. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which mayconnect to a global communications network, for example, the Internet.

When used in a LAN networking environment, the computer 602 is connectedto the LAN 652 through a wire and/or wireless communication networkinterface or adaptor 656. The adaptor 656 can facilitate wire and/orwireless communications to the LAN 652, which may also include awireless access point disposed thereon for communicating with thewireless functionality of the adaptor 656.

When used in a WAN networking environment, the computer 602 can includea modem 658, or is connected to a communications server on the WAN 654,or has other means for establishing communications over the WAN 654,such as by way of the Internet. The modem 658, which can be internal orexternal and a wire and/or wireless device, connects to the system bus608 via the input device interface 642. In a networked environment,program modules depicted relative to the computer 602, or portionsthereof, can be stored in the remote memory/storage device 650. It willbe appreciated that the network connections shown are exemplary andother means of establishing a communications link between the computerscan be used.

The computer 602 is operable to communicate with wire and wirelessdevices or entities using the IEEE 802 family of standards, such aswireless devices operatively disposed in wireless communication (e.g.,IEEE 802.16 over-the-air modulation techniques). This includes at leastWi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wirelesstechnologies, among others. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices. Wi-Fi networks use radiotechnologies called IEEE 802.11x (a, b, g, n, etc.) to provide secure,reliable, fast wireless connectivity. A Wi-Fi network can be used toconnect computers to each other, to the Internet, and to wire networks(which use IEEE 802.3-related media and functions).

FIG. 7 illustrates a block diagram of an exemplary communicationsarchitecture 700 suitable for implementing various embodiments aspreviously described, such as virtual machine migration. Thecommunications architecture 700 includes various common communicationselements, such as a transmitter, receiver, transceiver, radio, networkinterface, baseband processor, antenna, amplifiers, filters, powersupplies, and so forth. The embodiments, however, are not limited toimplementation by the communications architecture 700.

As shown in FIG. 7, the communications architecture 700 comprisesincludes one or more clients 702 and servers 704. In some embodimentscommunications architecture may include or implement one or moreportions of components, applications, and/or techniques describedherein. The clients 702 and the servers 704 are operatively connected toone or more respective client data stores 708 and server data stores 710that can be employed to store information local to the respectiveclients 702 and servers 704, such as cookies and/or associatedcontextual information. In various embodiments, any one of servers 704may implement one or more of logic flows or operations described hereinin conjunction with storage of data received from any one of clients 702on any of server data stores 710. In one or more embodiments, one ormore of client data store(s) 708 or server data store(s) 710 may includememory accessible to one or more portions of components, applications,and/or techniques described herein.

The clients 702 and the servers 704 may communicate information betweeneach other using a communication framework 706. The communicationsframework 706 may implement any well-known communications techniques andprotocols. The communications framework 706 may be implemented as apacket-switched network (e.g., public networks such as the Internet,private networks such as an enterprise intranet, and so forth), acircuit-switched network (e.g., the public switched telephone network),or a combination of a packet-switched network and a circuit-switchednetwork (with suitable gateways and translators).

The communications framework 706 may implement various networkinterfaces arranged to accept, communicate, and connect to acommunications network. A network interface may be regarded as aspecialized form of an input output interface. Network interfaces mayemploy connection protocols including without limitation direct connect,Ethernet (e.g., thick, thin, twisted pair 10/100/1900 Base T, and thelike), token ring, wireless network interfaces, cellular networkinterfaces, IEEE 802.11a-x network interfaces, IEEE 802.16 networkinterfaces, IEEE 802.20 network interfaces, and the like. Further,multiple network interfaces may be used to engage with variouscommunications network types. For example, multiple network interfacesmay be employed to allow for the communication over broadcast,multicast, and unicast networks. Should processing requirements dictatea greater amount speed and capacity, distributed network controllerarchitectures may similarly be employed to pool, load balance, andotherwise increase the communicative bandwidth required by clients 702and the servers 704. A communications network may be any one and thecombination of wired and/or wireless networks including withoutlimitation a direct interconnection, a secured custom connection, aprivate network (e.g., an enterprise intranet), a public network (e.g.,the Internet), a Personal Area Network (PAN), a Local Area Network(LAN), a Metropolitan Area Network (MAN), an Operating Missions as Nodeson the Internet (OMNI), a Wide Area Network (WAN), a wireless network, acellular network, and other communications networks.

Various embodiments may be implemented using hardware elements, softwareelements, or a combination of both. Examples of hardware elements mayinclude processors, microprocessors, circuits, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), logic gates, registers, semiconductor device, chips,microchips, chip sets, and so forth. Examples of software may includesoftware components, programs, applications, computer programs,application programs, system programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces,application program interfaces (API), instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to varioususers or manufacturing facilities to load into the fabrication machinesthat actually make the logic or processor. Some embodiments may beimplemented, for example, using a machine-readable medium or articlewhich may store an instruction or a set of instructions that, ifexecuted by a machine, may cause the machine to perform a method and/oroperations in accordance with the embodiments. Such a machine mayinclude, for example, any suitable processing platform, computingplatform, computing device, processing device, computing system,processing system, computer, processor, or the like, and may beimplemented using any suitable combination of hardware and/or software.The machine-readable medium or article may include, for example, anysuitable type of memory unit, memory device, memory article, memorymedium, storage device, storage article, storage medium and/or storageunit, for example, memory, removable or non-removable media, erasable ornon-erasable media, writeable or re-writeable media, digital or analogmedia, hard disk, floppy disk, Compact Disk Read Only Memory (CD-ROM),Compact Disk Recordable (CD-R), Compact Disk Rewriteable (CD-RW),optical disk, magnetic media, magneto-optical media, removable memorycards or disks, various types of Digital Versatile Disk (DVD), a tape, acassette, or the like. The instructions may include any suitable type ofcode, such as source code, compiled code, interpreted code, executablecode, static code, dynamic code, encrypted code, and the like,implemented using any suitable high-level, low-level, object-oriented,visual, compiled and/or interpreted programming language.

1. An apparatus, comprising: a processor; and a memory comprising instructions that when executed by the processor cause the processor to: determine a set of current resource utilizations for each candidate facility in a set of candidate facilities based on status data that is automatically updated with data generated by one or more sensors located at one or more candidate facilities in the set of candidate facilities, wherein the set of current resource utilizations for a respective candidate facility includes an availability of at least one of two or more requisite resources to provide a product requested by a user; evaluate the set of current resource utilizations for each candidate facility in the set of candidate facilities with a machine learning model to determine a set of available facilities; determine one or more recommended facilities based on the set of available facilities and an estimated completion time to fulfill the request for at least one available facility in the set of available facilities; and generate output comprising the one or more recommended facilities.
 2. The apparatus of claim 1, the memory comprising instructions that when executed by the processor cause the processor to determine the set of current resource utilizations for a candidate facility in the set of candidate facilities based on one or more of a number of appointments at the candidate facility, a number of existing requests at the candidate facility, a number of scheduled activities for requisite resources at the candidate facility, a number of customers en route to the candidate facility, a size of a check in queue at the candidate facility, a number of vehicles in a parking lot of the candidate facility, and a number of navigation systems with the candidate facility as a destination.
 3. The apparatus of claim 1, the memory comprising instructions that when executed by the processor cause the processor to generate output comprising the one or more recommended facilities and the estimated completion time for each of the one or more recommended facilities, the output to cause a user interface to present the one or more recommended facilities and the estimated completion time for each of the one or more recommended facilities to the user for selection.
 4. The apparatus of claim 3, the memory comprising instructions that when executed by the processor cause the processor to: identify a selected facility from the one or more recommended facilities based on input received via the user interface; and communicate the request for the product to the selected facility.
 5. The apparatus of claim 1, the memory comprising instructions that when executed by the processor cause the processor to determine one or more future staffing requirements for an available facility in the set of available facilities based on the set of current resource utilizations for the available facility in the set of available facilities.
 6. The apparatus of claim 1, wherein the two or more requisite resources to provide the product requested by the user comprise a device including one or more of an automated teller machine, a money counter, a vault, safety deposit boxes, coffee machine, a certified check printer, a bank card encoder, and a bank card blank.
 7. The apparatus of claim 1, wherein the two or more requisite resources to provide the product requested by the user comprise a skill including one or more of mortgage sales, small business banking, teller, public notary, and training certification.
 8. The apparatus of claim 1, wherein the machine learning model is trained on historical resource utilizations for one or more candidate facilities in the set of candidate facilities.
 9. At least one non-transitory computer-readable medium comprising a set of instructions that, in response to being executed by a processor circuit, cause the processor circuit to: determine a set of current resource utilizations for each candidate facility in a set of candidate facilities based on status data that is automatically updated with data generated by one or more sensors located at one or more candidate facilities in the set of candidate facilities, wherein the set of current resource utilizations for a respective candidate facility includes an availability of at least one of two or more requisite resources to provide a product requested by a user; evaluate the set of current resource utilizations for each candidate facility in the set of candidate facilities with a machine learning model to determine a set of available facilities; determine one or more recommended facilities based on the set of available facilities and an estimated completion time to fulfill the request for at least one available facility in the set of available facilities; and generate output comprising the one or more recommended facilities.
 10. The at least one non-transitory computer-readable medium of claim 9, comprising instructions that, in response to being executed by the processor circuit, cause the processor circuit to determine the set of current resource utilizations for a candidate facility in the set of candidate facilities based on one or more of a number of appointments at the candidate facility, a number of existing requests at the candidate facility, a number of scheduled activities for requisite resources at the candidate facility, a number of customers en route to the candidate facility, a size of a check in queue at the candidate facility, a number of vehicles in a parking lot of the candidate facility, and a number of navigation systems with the candidate facility as a destination.
 11. The at least one non-transitory computer-readable medium of claim 9, comprising instructions that, in response to being executed by the processor circuit, cause the processor circuit to generate output comprising the one or more recommended facilities and the estimated completion time for each of the one or more recommended facilities, the output to cause a user interface to present the one or more recommended facilities and the estimated completion time for each of the one or more recommended facilities to the user for selection.
 12. The at least one non-transitory computer-readable medium of claim 11, comprising instructions that, in response to being executed by the processor circuit, cause the processor circuit to: identify a selected facility from the one or more recommended facilities based on input received via the user interface; and communicate the request for the product to the selected facility.
 13. The at least one non-transitory computer-readable medium of claim 9, comprising instructions that, in response to being executed by the processor circuit, cause the processor circuit to determine one or more future staffing requirements for an available facility in the set of available facilities based on the set of current resource utilizations for the available facility in the set of available facilities.
 14. The at least one non-transitory computer-readable medium of claim 9, wherein the two or more requisite resources to provide the product requested by the user comprise a device including one or more of an automated teller machine, a money counter, a vault, safety deposit boxes, coffee machine, a certified check printer, a bank card encoder, and a bank card blank.
 15. The at least one non-transitory computer-readable medium of claim 9, wherein the two or more requisite resources to provide the product requested by the user comprise a skill including one or more of mortgage sales, small business banking, teller, public notary, and training certification.
 16. A computer-implemented method, comprising: determining a set of current resource utilizations for each candidate facility in a set of candidate facilities based on status data that is automatically updated with data generated by one or more sensors located at one or more candidate facilities in the set of candidate facilities, wherein the set of current resource utilizations for a respective candidate facility includes an availability of at least one of two or more requisite resources to provide a product requested by a user; evaluating the set of current resource utilizations for each candidate facility in the set of candidate facilities with a machine learning model to determine a set of available facilities; determining one or more recommended facilities based on the set of available facilities and an estimated completion time to fulfill the request for at least one available facility in the set of available facilities; and generating output comprising the one or more recommended facilities.
 17. The computer-implemented method of claim 16, comprising determining the set of current resource utilizations for a candidate facility in the set of candidate facilities based on one or more of a number of appointments at the candidate facility, a number of existing requests at the candidate facility, a number of scheduled activities for requisite resources at the candidate facility, a number of customers en route to the candidate facility, a size of a check in queue at the candidate facility, a number of vehicles in a parking lot of the candidate facility, and a number of navigation systems with the candidate facility as a destination.
 18. The computer-implemented method of claim 16, comprising generating output comprising the one or more recommended facilities and the estimated completion time for each of the one or more recommended facilities, the output to cause a user interface to present the one or more recommended facilities and the estimated completion time for each of the one or more recommended facilities to the user for selection.
 19. The computer-implemented method of claim 18, comprising: identifying a selected facility from the one or more recommended facilities based on input received via the user interface; and communicating the request for the product to the selected facility.
 20. The computer-implemented method of claim 16, comprising determining one or more future staffing requirements for an available facility in the set of available facilities based on the set of current resource utilizations for the available facility in the set of available facilities. 